Sukrit Gupta

Encoding and Decoding of Brain Dynamic Functional Connectivity for ADHD Diagnosis

This paper introduces BRAINMAP, a model for ADHD diagnosis using dynamic functional connectivity, achieving improved detection with three fMRI datasets.

BoMBR: An Annotated Bone Marrow Biopsy Dataset for Segmentation of Reticulin Fibers

The paper introduces BoMBR, a dataset of 201 annotated bone marrow biopsy images for reticulin fiber segmentation, and demonstrates a deep learning model achieving high accuracy in reticulin quantification and marrow fibrosis grading.

BaMBo: An Annotated Bone Marrow Biopsy Dataset for Segmentation Task

The paper introduces BaMBo, a bone marrow biopsy dataset for automated cellularity calculation, and presents a U-Net model achieving 83.1% Dice score and 5.9% cellularity prediction error.

PRESTIGE-ST: Patch Resolution and Encoder STrategies for Inference of Gene Expression from Spatial Transcriptomics

This study evaluates CNN and ViT models for predicting gene expression from spatial transcriptomics images, finding that compact CNNs with larger patches perform better for biologically relevant genes.

PEPerMINT: Peptide Abundance Imputation in Mass Spectrometry-based Proteomics using Graph Neural Networks

PEPerMINT imputes missing peptide abundance values in proteomics using graph neural networks, outperforming existing methods across multiple datasets and missingness scenarios.

Obtaining leaner deep neural networks for decoding brain functional connectome in a single shot

This paper proposes a method to simplify deep neural networks for decoding brain functional connectomes, achieving similar performance with 2% of the original parameters and identifying relevant biomarkers.

Decoding brain functional connectivity implicated in AD and MCI

The paper uses deep neural networks to classify brain states in AD, MCI, and CN subjects using fMRI data and identifies salient brain regions associated with these conditions.